Overview

Dataset statistics

Number of variables19
Number of observations7905
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory217.1 B

Variable types

Numeric11
Boolean5
Categorical3

Alerts

is_male is highly imbalanced (62.7%)Imbalance
Ascites is highly imbalanced (72.2%)Imbalance
Edema is highly imbalanced (65.7%)Imbalance

Reproduction

Analysis started2023-12-28 00:13:08.158108
Analysis finished2023-12-28 00:13:21.772679
Duration13.61 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

N_years
Real number (ℝ)

Distinct461
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5621187
Minimum0.11232877
Maximum13.136986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:21.881015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.11232877
5-th percentile0.91506849
Q13.369863
median5.0164384
Q37.3671233
95-th percentile11.306849
Maximum13.136986
Range13.024658
Interquartile range (IQR)3.9972603

Descriptive statistics

Standard deviation2.9979007
Coefficient of variation (CV)0.53898539
Kurtosis-0.49401726
Mean5.5621187
Median Absolute Deviation (MAD)1.9835616
Skewness0.44865975
Sum43968.548
Variance8.9874084
MonotonicityNot monotonic
2023-12-28T03:43:22.016397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.331506849 117
 
1.5%
3.928767123 105
 
1.3%
2.106849315 83
 
1.0%
9.438356164 73
 
0.9%
4.835616438 64
 
0.8%
4.890410959 64
 
0.8%
3.734246575 60
 
0.8%
2.476712329 59
 
0.7%
0.9150684932 58
 
0.7%
6.284931507 56
 
0.7%
Other values (451) 7166
90.7%
ValueCountFrequency (%)
0.1123287671 13
0.2%
0.1397260274 16
0.2%
0.1945205479 14
0.2%
0.2082191781 1
 
< 0.1%
0.2109589041 21
0.3%
0.2136986301 1
 
< 0.1%
0.295890411 1
 
< 0.1%
0.301369863 25
0.3%
0.3315068493 1
 
< 0.1%
0.3397260274 1
 
< 0.1%
ValueCountFrequency (%)
13.1369863 7
 
0.1%
12.48219178 51
0.6%
12.39178082 15
 
0.2%
12.35342466 41
0.5%
12.32876712 28
0.4%
12.23835616 14
 
0.2%
12.21643836 19
 
0.2%
12.2 22
0.3%
12.12876712 14
 
0.2%
12.03287671 1
 
< 0.1%

Age
Real number (ℝ)

Distinct391
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.337388
Minimum26.29589
Maximum78.493151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:22.125479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26.29589
5-th percentile33.717808
Q142.668493
median51.268493
Q356.668493
95-th percentile67.457534
Maximum78.493151
Range52.19726
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.082079
Coefficient of variation (CV)0.20029007
Kurtosis-0.49738238
Mean50.337388
Median Absolute Deviation (MAD)7.1342466
Skewness0.084091298
Sum397917.05
Variance101.64831
MonotonicityNot monotonic
2023-12-28T03:43:22.236446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.28493151 79
 
1.0%
61.3369863 71
 
0.9%
56.66849315 71
 
0.9%
52.21917808 70
 
0.9%
44.6 66
 
0.8%
56.05205479 65
 
0.8%
52.72876712 62
 
0.8%
38.79726027 62
 
0.8%
62.90410959 61
 
0.8%
63.92054795 61
 
0.8%
Other values (381) 7237
91.5%
ValueCountFrequency (%)
26.29589041 18
0.2%
28.90410959 17
0.2%
29.57534247 7
 
0.1%
29.61643836 1
 
< 0.1%
30.02191781 1
 
< 0.1%
30.29589041 33
0.4%
30.59452055 10
 
0.1%
30.88493151 19
0.2%
31.04109589 1
 
< 0.1%
31.40273973 19
0.2%
ValueCountFrequency (%)
78.49315068 36
0.5%
76.76164384 5
 
0.1%
75.0630137 22
0.3%
75.05205479 1
 
< 0.1%
74.62739726 1
 
< 0.1%
74.57534247 23
0.3%
72.82191781 8
 
0.1%
72.78630137 1
 
< 0.1%
71.94246575 13
 
0.2%
70.95616438 20
0.3%

is_male
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
7336 
True
 
569
ValueCountFrequency (%)
False 7336
92.8%
True 569
 
7.2%
2023-12-28T03:43:22.337483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Ascites
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
7525 
True
 
380
ValueCountFrequency (%)
False 7525
95.2%
True 380
 
4.8%
2023-12-28T03:43:22.411317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
True
4042 
False
3863 
ValueCountFrequency (%)
True 4042
51.1%
False 3863
48.9%
2023-12-28T03:43:22.485908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Spiders
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
5966 
True
1939 
ValueCountFrequency (%)
False 5966
75.5%
True 1939
 
24.5%
2023-12-28T03:43:22.562598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Edema
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size447.9 KiB
N
7161 
S
 
399
Y
 
345

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7905
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Length

2023-12-28T03:43:22.653350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T03:43:22.770950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
n 7161
90.6%
s 399
 
5.0%
y 345
 
4.4%

Most occurring characters

ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7905
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Bilirubin
Real number (ℝ)

Distinct111
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5944845
Minimum0.3
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:22.931280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.7
median1.1
Q33
95-th percentile11
Maximum28
Range27.7
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation3.8129603
Coefficient of variation (CV)1.4696408
Kurtosis12.908824
Mean2.5944845
Median Absolute Deviation (MAD)0.5
Skewness3.3396953
Sum20509.4
Variance14.538666
MonotonicityNot monotonic
2023-12-28T03:43:23.054363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 847
 
10.7%
0.7 653
 
8.3%
0.8 613
 
7.8%
0.9 608
 
7.7%
0.5 552
 
7.0%
1.1 443
 
5.6%
1.3 368
 
4.7%
1 292
 
3.7%
0.4 180
 
2.3%
1.4 175
 
2.2%
Other values (101) 3174
40.2%
ValueCountFrequency (%)
0.3 52
 
0.7%
0.4 180
 
2.3%
0.5 552
7.0%
0.6 847
10.7%
0.7 653
8.3%
0.8 613
7.8%
0.9 608
7.7%
1 292
 
3.7%
1.1 443
5.6%
1.2 166
 
2.1%
ValueCountFrequency (%)
28 13
0.2%
25.5 13
0.2%
24.5 16
0.2%
22.5 16
0.2%
21.9 1
 
< 0.1%
21.6 19
0.2%
21.4 1
 
< 0.1%
20 4
 
0.1%
18 4
 
0.1%
17.9 9
0.1%

Cholesterol
Real number (ℝ)

Distinct226
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.56192
Minimum120
Maximum1775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:23.193441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile198
Q1248
median298
Q3390
95-th percentile646
Maximum1775
Range1655
Interquartile range (IQR)142

Descriptive statistics

Standard deviation195.37934
Coefficient of variation (CV)0.5573319
Kurtosis18.162327
Mean350.56192
Median Absolute Deviation (MAD)62
Skewness3.6796575
Sum2771192
Variance38173.088
MonotonicityNot monotonic
2023-12-28T03:43:23.425069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448 152
 
1.9%
248 151
 
1.9%
263 143
 
1.8%
298 138
 
1.7%
232 131
 
1.7%
260 120
 
1.5%
257 117
 
1.5%
316 110
 
1.4%
236 109
 
1.4%
280 106
 
1.3%
Other values (216) 6628
83.8%
ValueCountFrequency (%)
120 10
 
0.1%
127 18
 
0.2%
132 36
0.5%
134 1
 
< 0.1%
149 7
 
0.1%
151 9
 
0.1%
168 9
 
0.1%
172 19
 
0.2%
174 20
 
0.3%
175 58
0.7%
ValueCountFrequency (%)
1775 11
0.1%
1712 19
0.2%
1600 22
0.3%
1492 1
 
< 0.1%
1480 11
0.1%
1436 1
 
< 0.1%
1336 9
0.1%
1276 21
0.3%
1236 1
 
< 0.1%
1128 14
0.2%

Albumin
Real number (ℝ)

Distinct160
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5483226
Minimum1.96
Maximum4.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:23.590425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.96
5-th percentile2.97
Q13.35
median3.58
Q33.77
95-th percentile4.08
Maximum4.64
Range2.68
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.34617081
Coefficient of variation (CV)0.097559002
Kurtosis1.3396217
Mean3.5483226
Median Absolute Deviation (MAD)0.21
Skewness-0.5611495
Sum28049.49
Variance0.11983423
MonotonicityNot monotonic
2023-12-28T03:43:23.770306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.35 370
 
4.7%
3.6 368
 
4.7%
3.7 326
 
4.1%
3.85 255
 
3.2%
3.5 223
 
2.8%
3.77 217
 
2.7%
3.26 195
 
2.5%
3.65 183
 
2.3%
3.61 166
 
2.1%
3.2 161
 
2.0%
Other values (150) 5441
68.8%
ValueCountFrequency (%)
1.96 4
 
0.1%
2.1 4
 
0.1%
2.23 3
 
< 0.1%
2.27 4
 
0.1%
2.31 4
 
0.1%
2.33 16
 
0.2%
2.35 1
 
< 0.1%
2.43 50
0.6%
2.52 1
 
< 0.1%
2.53 9
 
0.1%
ValueCountFrequency (%)
4.64 20
0.3%
4.52 5
 
0.1%
4.4 14
 
0.2%
4.38 24
0.3%
4.34 1
 
< 0.1%
4.31 1
 
< 0.1%
4.3 42
0.5%
4.26 1
 
< 0.1%
4.24 12
 
0.2%
4.23 19
0.2%

Copper
Real number (ℝ)

Distinct171
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.902846
Minimum4
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:23.915086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q139
median63
Q3102
95-th percentile231
Maximum588
Range584
Interquartile range (IQR)63

Descriptive statistics

Standard deviation75.899266
Coefficient of variation (CV)0.90460895
Kurtosis10.21299
Mean83.902846
Median Absolute Deviation (MAD)26
Skewness2.7017358
Sum663252
Variance5760.6986
MonotonicityNot monotonic
2023-12-28T03:43:24.023779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 311
 
3.9%
52 303
 
3.8%
39 216
 
2.7%
58 207
 
2.6%
75 188
 
2.4%
41 179
 
2.3%
13 172
 
2.2%
20 169
 
2.1%
44 154
 
1.9%
38 151
 
1.9%
Other values (161) 5855
74.1%
ValueCountFrequency (%)
4 12
 
0.2%
5 2
 
< 0.1%
9 53
 
0.7%
10 25
 
0.3%
11 60
 
0.8%
12 36
 
0.5%
13 172
2.2%
14 42
 
0.5%
15 11
 
0.1%
16 7
 
0.1%
ValueCountFrequency (%)
588 19
0.2%
558 7
 
0.1%
464 26
0.3%
456 1
 
< 0.1%
444 21
0.3%
412 13
 
0.2%
380 43
0.5%
358 21
0.3%
308 4
 
0.1%
290 20
0.3%

Alk_Phos
Real number (ℝ)

Distinct364
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1816.7452
Minimum289
Maximum13862.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:24.141090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum289
5-th percentile614
Q1834
median1181
Q31857
95-th percentile6064.8
Maximum13862.4
Range13573.4
Interquartile range (IQR)1023

Descriptive statistics

Standard deviation1903.7507
Coefficient of variation (CV)1.0478908
Kurtosis11.59975
Mean1816.7452
Median Absolute Deviation (MAD)460
Skewness3.1955577
Sum14361371
Variance3624266.6
MonotonicityNot monotonic
2023-12-28T03:43:24.330705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
663 117
 
1.5%
1345 81
 
1.0%
7277 79
 
1.0%
944 78
 
1.0%
794 76
 
1.0%
645 76
 
1.0%
1636 76
 
1.0%
1052 75
 
0.9%
2276 63
 
0.8%
674 63
 
0.8%
Other values (354) 7121
90.1%
ValueCountFrequency (%)
289 32
0.4%
310 10
 
0.1%
369 21
0.3%
377 17
0.2%
414 8
 
0.1%
423 31
0.4%
453 26
0.3%
466 16
0.2%
516 12
 
0.2%
554 31
0.4%
ValueCountFrequency (%)
13862.4 15
0.2%
13486.2 1
 
< 0.1%
12258.8 26
0.3%
11552 11
0.1%
11320.2 15
0.2%
11046.6 12
0.2%
10795.4 1
 
< 0.1%
10396.8 22
0.3%
10165 11
0.1%
9933.2 3
 
< 0.1%

SGOT
Real number (ℝ)

Distinct206
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.6046
Minimum26.35
Maximum457.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:24.593748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26.35
5-th percentile54.25
Q175.95
median108.5
Q3137.95
95-th percentile198.4
Maximum457.25
Range430.9
Interquartile range (IQR)62

Descriptive statistics

Standard deviation48.790945
Coefficient of variation (CV)0.42573286
Kurtosis5.8167874
Mean114.6046
Median Absolute Deviation (MAD)31
Skewness1.5348057
Sum905949.38
Variance2380.5563
MonotonicityNot monotonic
2023-12-28T03:43:24.724315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.3 256
 
3.2%
57.35 247
 
3.1%
137.95 206
 
2.6%
120.9 198
 
2.5%
97.65 189
 
2.4%
170.5 184
 
2.3%
93 178
 
2.3%
128.65 170
 
2.2%
66.65 138
 
1.7%
106.95 137
 
1.7%
Other values (196) 6002
75.9%
ValueCountFrequency (%)
26.35 8
 
0.1%
28.38 12
 
0.2%
40.6 1
 
< 0.1%
41.85 16
 
0.2%
43.4 40
0.5%
45 14
 
0.2%
46.5 6
 
0.1%
49.6 52
0.7%
51.15 57
0.7%
52 15
 
0.2%
ValueCountFrequency (%)
457.25 17
0.2%
338 9
0.1%
328.6 15
0.2%
299.15 6
 
0.1%
288 9
0.1%
280.55 15
0.2%
272.8 9
0.1%
260.15 1
 
< 0.1%
253 1
 
< 0.1%
246.45 13
0.2%

Tryglicerides
Real number (ℝ)

Distinct154
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.34016
Minimum33
Maximum598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:24.827406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile56
Q184
median104
Q3139
95-th percentile210
Maximum598
Range565
Interquartile range (IQR)55

Descriptive statistics

Standard deviation52.530402
Coefficient of variation (CV)0.45543894
Kurtosis15.048118
Mean115.34016
Median Absolute Deviation (MAD)27
Skewness2.6339208
Sum911764
Variance2759.4431
MonotonicityNot monotonic
2023-12-28T03:43:24.930229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 262
 
3.3%
85 223
 
2.8%
91 218
 
2.8%
118 211
 
2.7%
68 188
 
2.4%
56 187
 
2.4%
146 181
 
2.3%
108 175
 
2.2%
55 171
 
2.2%
133 170
 
2.2%
Other values (144) 5919
74.9%
ValueCountFrequency (%)
33 13
 
0.2%
44 37
 
0.5%
46 12
 
0.2%
49 13
 
0.2%
50 19
 
0.2%
52 24
 
0.3%
53 15
 
0.2%
55 171
2.2%
56 187
2.4%
57 10
 
0.1%
ValueCountFrequency (%)
598 13
0.2%
432 16
0.2%
393 1
 
< 0.1%
382 4
 
0.1%
322 5
 
0.1%
319 15
0.2%
318 18
0.2%
309 20
0.3%
283 1
 
< 0.1%
280 20
0.3%

Platelets
Real number (ℝ)

Distinct227
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.22897
Minimum62
Maximum563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:25.035551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile128
Q1211
median265
Q3316
95-th percentile430
Maximum563
Range501
Interquartile range (IQR)105

Descriptive statistics

Standard deviation87.465579
Coefficient of variation (CV)0.32977385
Kurtosis0.33057783
Mean265.22897
Median Absolute Deviation (MAD)53
Skewness0.42004793
Sum2096635
Variance7650.2274
MonotonicityNot monotonic
2023-12-28T03:43:25.157748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
344 233
 
2.9%
228 159
 
2.0%
268 158
 
2.0%
295 154
 
1.9%
336 147
 
1.9%
251 144
 
1.8%
265 138
 
1.7%
269 136
 
1.7%
213 136
 
1.7%
309 132
 
1.7%
Other values (217) 6368
80.6%
ValueCountFrequency (%)
62 11
 
0.1%
65 1
 
< 0.1%
70 10
 
0.1%
71 15
 
0.2%
76 1
 
< 0.1%
79 18
0.2%
80 25
0.3%
81 11
 
0.1%
88 3
 
< 0.1%
95 38
0.5%
ValueCountFrequency (%)
563 36
0.5%
539 5
 
0.1%
518 14
 
0.2%
515 2
 
< 0.1%
514 13
 
0.2%
493 17
0.2%
487 10
 
0.1%
474 17
0.2%
471 24
0.3%
467 40
0.5%

Prothrombin
Real number (ℝ)

Distinct49
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.629462
Minimum9
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2023-12-28T03:43:25.290912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.6
Q110
median10.6
Q311
95-th percentile12
Maximum18
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78173483
Coefficient of variation (CV)0.073544155
Kurtosis4.288955
Mean10.629462
Median Absolute Deviation (MAD)0.5
Skewness1.292436
Sum84025.9
Variance0.61110934
MonotonicityNot monotonic
2023-12-28T03:43:25.438595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10.6 1070
 
13.5%
11 842
 
10.7%
10 638
 
8.1%
9.9 517
 
6.5%
9.8 440
 
5.6%
10.1 390
 
4.9%
10.9 339
 
4.3%
11.5 295
 
3.7%
9.6 288
 
3.6%
10.2 283
 
3.6%
Other values (39) 2803
35.5%
ValueCountFrequency (%)
9 8
 
0.1%
9.1 9
 
0.1%
9.2 5
 
0.1%
9.3 8
 
0.1%
9.4 17
 
0.2%
9.5 137
 
1.7%
9.6 288
3.6%
9.7 199
 
2.5%
9.8 440
5.6%
9.9 517
6.5%
ValueCountFrequency (%)
18 1
 
< 0.1%
17.1 2
 
< 0.1%
15.2 12
 
0.2%
14.1 4
 
0.1%
13.6 9
 
0.1%
13.4 1
 
< 0.1%
13.3 6
 
0.1%
13.2 32
0.4%
13.1 1
 
< 0.1%
13 45
0.6%

Stage
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size463.3 KiB
3.0
3153 
4.0
2703 
2.0
1652 
1.0
397 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 3153
39.9%
4.0 2703
34.2%
2.0 1652
20.9%
1.0 397
 
5.0%

Length

2023-12-28T03:43:25.559153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T03:43:25.661528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 3153
39.9%
4.0 2703
34.2%
2.0 1652
20.9%
1.0 397
 
5.0%

Most occurring characters

ValueCountFrequency (%)
. 7905
33.3%
0 7905
33.3%
3 3153
 
13.3%
4 2703
 
11.4%
2 1652
 
7.0%
1 397
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7905
50.0%
3 3153
 
19.9%
4 2703
 
17.1%
2 1652
 
10.4%
1 397
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7905
33.3%
0 7905
33.3%
3 3153
 
13.3%
4 2703
 
11.4%
2 1652
 
7.0%
1 397
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7905
33.3%
0 7905
33.3%
3 3153
 
13.3%
4 2703
 
11.4%
2 1652
 
7.0%
1 397
 
1.7%

Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size448.1 KiB
C
4965 
D
2665 
CL
 
275

Length

Max length2
Median length1
Mean length1.0347881
Min length1

Characters and Unicode

Total characters8180
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowC
3rd rowD
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 4965
62.8%
D 2665
33.7%
CL 275
 
3.5%

Length

2023-12-28T03:43:25.772768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T03:43:25.865022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
c 4965
62.8%
d 2665
33.7%
cl 275
 
3.5%

Most occurring characters

ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8180
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 8180
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

took_drug
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
4010 
True
3895 
ValueCountFrequency (%)
False 4010
50.7%
True 3895
49.3%
2023-12-28T03:43:25.946530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2023-12-28T03:43:19.996942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:08.832852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.698016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.638276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:11.690269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:12.974081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:14.433080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:15.700982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.093212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.969871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.853099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:20.191850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.066284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.768542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.716213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:11.773467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:13.137329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:14.592450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:15.961716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.197466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.044782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.943532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:20.334178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.135676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.833647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.791204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:11.845377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:13.299967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:14.710523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:16.046893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.287938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.119569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:19.049453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:20.440962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.196074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.901322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.866117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:12.007845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:13.449154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:14.804726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:16.129382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.374414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.199530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:19.194413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:20.531421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.262003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.971793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.978543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:12.252769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:13.564079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:14.885929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:16.222520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.465014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.281999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:19.311131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:20.788062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.322779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.043273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:11.097200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:12.434402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:13.679916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:14.956721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:16.307483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.539940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.361773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:19.411113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:20.864888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.389273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.131996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:11.194223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:12.554198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:13.777214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:15.027396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:16.413687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.617224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.446188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:19.494360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:20.955270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.454988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.263002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:11.291979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:12.654024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:13.873683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:15.109959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:16.598547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.699824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.536998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:19.579123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:21.049847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.511935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.388523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:11.468169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:12.727640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:13.965969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:15.195590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:16.743727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.766446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.611992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:19.648573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:21.191874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.572828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.487437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:11.545178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:12.811160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:14.130913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:15.421761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:16.860192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.842169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.694300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:19.746546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:21.297351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:09.633468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:10.563913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:11.615628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:12.883931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:14.288404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:15.573329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:16.977816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:17.905689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:18.772785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T03:43:19.861646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-28T03:43:21.433570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-28T03:43:21.661091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

N_yearsAgeis_maleAscitesHepatomegalySpidersEdemaBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStageStatustook_drug
02.73698658.991781TrueFalseFalseFalseN2.3316.03.35172.01601.0179.8063.0394.09.73.0DTrue
17.05205552.704110FalseFalseFalseFalseN0.9364.03.5463.01440.0134.8588.0361.011.03.0CFalse
29.39178137.608219FalseFalseTrueTrueY3.3299.03.55131.01029.0119.3550.0199.011.74.0DFalse
37.05753450.575342FalseFalseFalseFalseN0.6256.03.5058.01653.071.3096.0269.010.73.0CFalse
42.15890445.638356FalseFalseTrueFalseN1.1346.03.6563.01181.0125.5596.0298.010.64.0CFalse
51.92602752.794521FalseFalseTrueFalseN0.6227.03.4634.06456.260.6368.0213.011.53.0DTrue
63.56164448.501370FalseFalseFalseFalseN1.0328.03.3543.01677.0137.9590.0291.09.83.0CFalse
74.42465858.304110FalseFalseTrueFalseN0.6273.03.9436.0598.052.70214.0227.09.93.0CFalse
85.61643856.668493FalseFalseFalseFalseN0.7360.03.6572.03196.094.55154.0269.09.82.0CTrue
97.16438441.120548FalseFalseFalseFalseN0.9478.03.6039.01758.0171.00140.0234.010.62.0CTrue
N_yearsAgeis_maleAscitesHepatomegalySpidersEdemaBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStageStatustook_drug
78953.92602738.797260FalseFalseFalseFalseN0.5291.04.2437.01065.085.25195.0201.010.62.0CFalse
78963.48219237.824658FalseFalseFalseFalseN0.6328.03.9531.0663.052.70166.0344.010.43.0CFalse
78973.98630146.295890FalseFalseFalseTrueN3.4279.03.53143.0671.0113.1572.0151.09.83.0CFalse
78980.21095954.476712FalseTrueTrueFalseY5.1178.02.75464.01020.0120.90118.080.012.34.0DFalse
78993.87123367.457534FalseFalseFalseFalseN1.3262.03.7365.02045.089.9078.0181.011.03.0DFalse
79003.19452146.134247FalseFalseFalseFalseN0.8309.03.5638.01629.079.05224.0344.09.92.0CTrue
79014.08767146.660274FalseFalseTrueFalseN0.9260.03.4362.01440.0142.0078.0277.010.04.0CFalse
79024.31780870.884932FalseFalseFalseTrueS2.0225.03.1951.0933.069.7562.0200.012.72.0DTrue
79039.81917862.904110TrueFalseTrueFalseN0.7248.02.7532.01003.057.35118.0221.010.64.0DTrue
79045.41917852.704110FalseFalseFalseFalseN0.7256.03.2322.0645.074.4085.0336.010.33.0CTrue